This study presents a new powerful method for probability density function (PDF) estimation of the probabilistic power flow problem results. The proposed method works on a prior density function to improve its potential PDF estimation. The generalised cross-entropy method is used to refine the initial dataset. Most of the existing methods for PDF estimation do not provide a sparse result and rely on approximation and simplification. Compared to the other conventional methods, the proposed method has some remarkable features in density estimation. Low computational burden or high-speed response and high-precision results are the most prominent features of the proposed method. The method can deal with a correlated problem which is considered the correlation between uncertain parameters. It is tested on IEEE 14-bus and IEEE 118-bus systems, considering the non-stationary loads, some renewable energy resources and their correlations. The results of the proposed method are compared against more accurate results obtained from Monte Carlo simulation and some other conventional methods in density estimation. Comparisons confirm the high accuracy level and speed of the proposed density estimator against the other methods.